In situ multi-excitation chlorophyll fluorometer for phytoplankton measurements: Technologies and applications beyond conventional fluorometers

Yoshida, Mitsuo; Horiuchi, Tomohiro; Nagasawa, Yasuhiro

doi:10.23919/oceans.2011.6107049

Cited by 18 publications

(25 citation statements)

References 3 publications

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“…During the past decade, submersible spectrofluorometers, such as FluoroProbe (bbe moldaenke, USA) and Multi-Exciter (JFE Advantech Co., Ltd., Japan), were commercially developed to derive the phytoplankton composition by measuring in vivo F(λ) [15,16]. Because of their easy operation and rapid measurement, these spectrofluorometers have become increasingly popular in aquatic ecosystem investigations, for instance, assessing freshwater water quality [17], monitoring harmful algal blooms [18,19], and investigating the vertical distribution patterns of phytoplankton communities [20,21].…”

Section: Introductionmentioning

confidence: 99%

Statistical approach for the retrieval of phytoplankton community structures from in situ fluorescence measurements

et al. 2016

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Knowledge of phytoplankton community structures is important to the understanding of various marine biogeochemical processes and ecosystem. Fluorescence excitation spectra (F(λ)) provide great potential for studying phytoplankton communities because their spectral variability depends on changes in the pigment compositions related to distinct phytoplankton groups. Commercial spectrofluorometers have been developed to analyze phytoplankton communities by measuring the field F(λ), but estimations using the default methods are not always accurate because of their strong dependence on norm spectra, which are obtained by culturing pure algae of a given group and are assumed to be constant. In this study, we proposed a novel approach for estimating the chlorophyll a (Chl a) fractions of brown algae, cyanobacteria, green algae and cryptophytes based on a data set collected in the East China Sea (ECS) and the Tsushima Strait (TS), with concurrent measurements of in vivo F(λ) and phytoplankton communities derived from pigments analysis. The new approach blends various statistical features by computing the band ratios and continuum-removed spectra of F(λ) without requiring a priori knowledge of the norm spectra. The model evaluations indicate that our approach yields good estimations of the Chl a fractions, with root-mean-square errors of 0.117, 0.078, 0.072 and 0.060 for brown algae, cyanobacteria, green algae and cryptophytes, respectively. The statistical analysis shows that the models are generally robust to uncertainty in F(λ). We recommend using a site-specific model for more accurate estimations. To develop a site-specific model in the ECS and TS, approximately 26 samples are sufficient for using our approach, but this conclusion needs to be validated in additional regions. Overall, our approach provides a useful technical basis for estimating phytoplankton communities from measurements of F(λ).

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Section: Introductionmentioning

confidence: 99%

Statistical approach for the retrieval of phytoplankton community structures from in situ fluorescence measurements

et al. 2016

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“…It has been shown that different fluorescence probes provided different responses to turbidity depending on their specific optical configuration (Zamyadi et al ). Some probes showed a positive trend in the background fluorescence with increasing turbidity (Chang et al ; Yoshida et al ), while others showed an opposite trend per Chl a slope (Beutler et al ; Brient et al ; Zamyadi et al ). Even though most studies indicate that the effect is independent of the type of sediment (kaolinite, bentonite, or sand), Brient et al () showed that sand had a smaller effect than kaolinite did.…”

Section: Discussionmentioning

confidence: 99%

Interpretation of total phytoplankton and cyanobacteria fluorescence from cross‐calibrated fluorometers, including sensitivity to turbidity and colored dissolved organic matter

Cremella

Huot

Bonilla

2018

Limnology & Ocean Methods

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In vivo pigment fluorescence methods allow simple real-time detection and quantification of freshwater algae and cyanobacteria. Available models are still limited to high-cost fluorometers, validated for single instruments or individual water bodies, preventing data comparison between multiple instruments, and thus, restricting their use in large-scale monitoring programs. Moreover, few models include corrections for optical interference (water turbidity and colored dissolved organic matter, CDOM). In this study, we developed simple models to predict phytoplankton and cyanobacterial chlorophyll a (Chl a) concentrations based on Chl a and C-phycocyanin in vivo fluorescence, using multiple low-cost handheld fluorometers. We aimed to: (1) fit models to mixed cyanobacterial and microalgal cultures; (2) cross-calibrate nine fluorometers of the same brand and series; (3) correct the CDOM and turbidity effects; and (4) test the algorithms' performance with natural samples. We achieved comparable results between nine instruments after the cross-calibration, allowing their simultaneous use. We obtained algorithms for total and cyanobacterial Chl a estimation. We developed parametric corrections to remove CDOM and turbidity interferences in the algorithms. Five sampling sites (from a lake, a stream, and an estuary) were used to test the algorithms using eight cross-calibrated fluorometers. The models showed their best performance after CDOM and turbidity corrections (total Chl a: R 2 = 0.99, RMSE = 7.8 μg Chl a L −1 ; cyanobacterial Chl a: R 2 = 0.98, RMSE = 9.8 μg Chl a L −1 ). In summary, our models can quantify total phytoplankton and cyanobacterial Chl a in real time with multiple low-cost fluorometers, allowing its implementation in large-scale monitoring programs.

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“…However, it requires suitable calibration of pigment-taxonomy relationships using pure cultures of the target species. The Multi-Exciter also requires pre-or postcalibration using the spectral fluorescence features of pure culture samples of the target species (Yoshida et al 2011). During field sampling, it is difficult to predict those types of species that will appear at the observation site.…”

Section: Multi-excitermentioning

confidence: 99%

Changes in phytoplankton community structure during wind-induced fall bloom on the central Chukchi shelf

et al. 2018

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The recent increasing of atmospheric turbulence has had considerable impact on the oceanic environment and ecosystems of the Arctic. To understand its effect on phytoplankton community structure, a Eulerian fixed-point observation (FPO) was conducted on the Chukchi shelf in fall 2013. Temporal and vertical distributions of the phytoplankton community were inferred from algal pigment signatures. A strong wind event (SWE) occurred during the observation term, and significant convection supplied nutrients from the bottom layer to the surface. Before the SWE, pigment composition in the warmer, less saline, and nutrient-poor surface waters was diverse with low concentration of chlorophyll-a (chla). Vertical mixing induced by the SWE weakened the stratification and brought sufficient nutrients to enhance diatom-derived pigment concentrations (e.g., fucoxanthin and chlc3), suggesting increases in diatoms. We also developed a model to predict the distribution of major phytoplankton pigment/chla ratios using a profiling multi-wavelength fluorometer (Multi-Exciter) with higher spatio-temporal resolution. The Multi-Exciter also captured changes in pigment composition with environmental changes at the FPO site and at four observation sites 16 km from the location of the FPO. Furthermore, we investigated the change in grazing rates of the major Arctic copepod Calanus glacialis copepodid stage five to assess the interaction between primary and secondary producers during the fall bloom. Increased diatom biomass caused a significant increase in the grazing rate on microphytoplankton (> 20 µm) and a decrease on nanophytoplankton (2-20 µm), indicative of a strong cascade effect because of the reduction of microzooplankton due to the grazing from C. glacialis. We conclude that SWEs during fall might affect food webs via the alternation of seasonal succession of phytoplankton community structure.

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In situ multi-excitation chlorophyll fluorometer for phytoplankton measurements: Technologies and applications beyond conventional fluorometers

Cited by 18 publications

References 3 publications

Statistical approach for the retrieval of phytoplankton community structures from in situ fluorescence measurements

Statistical approach for the retrieval of phytoplankton community structures from in situ fluorescence measurements

Interpretation of total phytoplankton and cyanobacteria fluorescence from cross‐calibrated fluorometers, including sensitivity to turbidity and colored dissolved organic matter

Changes in phytoplankton community structure during wind-induced fall bloom on the central Chukchi shelf

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